Research Challenge team 1: Nanomedicines
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Primary Supervisor: Nicholas Warren (Sheffield)
Secondary Supervisor: Vanessa Hearnden and Zoltan Kis (Sheffield)
Research challenges addressed in this project:
Developing advanced nanoparticles is crucial for delivering sensitive pharmaceutical payloads such as proteins and nucleic acids. Such nanomedicines include new-generation vaccines which rely on lipid nanoparticles (LNPs), which are a complex mixture of components required to efficiently encapsulate and deliver the payload. However, there is huge potential for nanomedicines in a number of areas, but optimising these formulations is slow and significant challenges remain, including poor stability, high immunogenicity, and inefficient organ targeting.
To rapidly discover new formulations, the project will develop and implement a "self-driving laboratory" (SDL). This cyber-physical system, guided by machine learning, will radically accelerate the screening and optimisation of new components, such as novel ionisable lipids and functional polymers. The platform will integrate online analysis with offline and in vitro cell testing. The data collected will be fed to a Bayesian optimisation framework to efficiently identify formulations with the best balance of performance, cost, and quality, while also building a deeper mechanistic understanding of how hybrid formulations influence biological outcomes. The project suits an individual with a background in (bio)chemical science or engineering with an interest in learning about the implementation of digital tools for accelerating R&D. -
Primary Supervisor: Clare Hoskins (Strathclyde)
Secondary Supervisor: Iain Oswald and Tell Tuttle (Strathclyde)
Research challenges addressed in this project:
Solid lipid nanoparticles (SLNs) are a class of nanocarriers composed of biocompatible lipids that solidify at room and body temperature. These lipids self-assemble into a densely packed crystalline core, which is stabilised by a surrounding layer of surfactants. This structural arrangement provides a versatile platform for the encapsulation of hydrophobic drug molecules. During the rapid crystallisation process, imperfections and deformations form within the lipid matrix, creating spaces where drug molecules can be incorporated. These imperfections are crucial for drug loading, but they also introduce variability in drug release profiles.
One of the key challenges in SLN formulation is the polymorphic transformation of the lipid core. Initially, the lipids tend to crystallise into a metastable alpha polymorphic form. However, over time or under specific environmental conditions—such as elevated temperature, humidity, or mechanical stress—this alpha form gradually transitions into the more thermodynamically stable beta polymorph. This transformation alters the internal structure of the SLNs, potentially leading to the expulsion of drug molecules from the lipid matrix or, conversely, tighter entrapment of the drug within the crystalline lattice. Both outcomes can significantly influence the release kinetics of the encapsulated drug, thereby affecting its bioavailability and therapeutic efficacy.
This project aims to systematically investigate the polymorphic transitions in SLNs and their impact on drug release behaviour and to use this data to develop a predictive model that correlates lipid polymorphism with drug release profiles and overall formulation stability. -
Primary Supervisor: Yvonne Perrie (Strathclyde)
Secondary Supervisor: Alistair Florence (Strathclyde)
Research challenges addressed in this project:
RNA therapies are reshaping modern medicine, yet developing effective LNPs remains slow, costly, and often unpredictable. This project tackles that challenge by creating a new platform that can design and test LNPs with far greater precision and reliability than current lab methods. You will work at the forefront of formulation science and advanced biological testing, generating high-quality potency data that reveal how LNP properties influence real biological responses. By linking formulation attributes directly to potency, the project will uncover the design rules needed to create safer, more effective RNA medicines. This is a unique opportunity to contribute to the next generation of nanomedicines—accelerating discovery, reducing development failures, and ultimately helping innovative RNA treatments reach patients faster.
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Primary Supervisor: Yvonne Perrie (Strathclyde)
Secondary Supervisors: Blair Johnston and Clare Hoskins (Strathclyde)
Research challenges addressed in this project:
Are you interested in improving how new medicines are designed and tested? This PhD places you at the forefront of organ-on-a-chip research - systems that recreate aspects of human biology to generate more predictive data for drug development.
You will help build a platform that can run experiments automatically, deliver precise dosing schedules, and track tissue responses continuously. By improving efficiency and reproducibility, your work will support better decision-making in early-stage therapeutic development.
This project is ideal for applicants from biomedical sciences, engineering, pharmacy, chemistry, or related disciplines who enjoy hands-on experimental work and want to contribute to a rapidly advancing field. You’ll develop expertise in tissue models, continuous sensing, data-rich experimentation, and interdisciplinary collaboration - skills highly valued across academia and industry.
Research Challenge team 2: Drug substance
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Primary Supervisor: Alastair Florence (Strathclyde)
Secondary Supervisor: Martin Prostredny (Strathclyde)
Research challenges addressed in this project:
Join a pioneering PhD project to miniaturise the Crystallisation Screening DataFactory, enabling closed-loop, model-driven crystallisation insights at dramatically smaller scales for high-value, limited-availability materials. You’ll evaluate cutting-edge liquid-handling platforms and analytical workflows to generate high-quality thermodynamic and kinetic data that power CMAC’s Crystallisation Parameter Database and Knowledge Graph.
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Primary Supervisor: Jan Sefcik (Strathclyde)
Secondary Supervisor: Cameron Brown and Jenna Johnston (Strathclyde)
Research challenges addressed in this project:
Crystallisation defines particle structure at the molecular level, while wet milling refines particle size and shape, but their combined potential remains largely untapped. This PhD project aims to transform how particle attributes are engineered by integrating crystallisation and wet milling into a unified, data-driven process design framework. The student will investigate how these two operations interact, developing experimental workflows and test beds that couple in-situ monitoring with mechanistic understanding. Data based and physical modelling techniques will be applied to identify key process–structure relationships, optimise operating conditions, and enable predictive control of particle attributes. Working at the interface of chemical engineering, crystallisation science, and data analytics, the project offers the opportunity to create new hybrid manufacturing strategies that deliver consistent, high-quality particles for medicines manufacturing. The outcome will be a set of design principles and digital tools that guide the production of “perfect” particles tailored to specific performance requirements.
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Primary Supervisor: Karen Johnston (Strathclyde)
Secondary Supervisor: Paul Mulheran and Jan Sefcik (Strathclyde)
Research challenges addressed in this project:
Crystallisation is an important pharmaceutical purification process, and the presence of interfaces can enhance crystallisation of an active pharmaceutical ingredient (API) (desirable) or lead to fouling on equipment surfaces (undesirable). Nucleation is the initial stages of crystal formation, and the aim of this project is to develop a digital workflow for screening combinations of interfaces and solvents to optimise nucleation of a given API solid form. The project will combine molecular simulations, in situ interfacial measurements and nucleation kinetics measurements relating interfacial concentration enhancement and heterogenous nucleation kinetics.
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Primary Supervisor: Sven Schroeder (Leeds)
Secondary Supervisor: Richard Bourne, Roisin O'Connell and Blair Johnston (Leeds)
Research challenges addressed in this project:
We are working with the other partners in the CEDAR Centre to simplify and accelerate the manufacturing pipeline from the synthesis of active pharmaceutical ingredients (APIs) to their formulated product form by using modern AI-based control and optimisation. This project aims to create an efficient and sustainable API synthesis and crystallisation process that can handle the complex demands of both chemical synthesis and crystallisation. Contemporary strands of research in this area tend to focus either on data-driven (using. for example, machine learning, Bayesian optimisation, etc.) or the improvement of model-based (deterministic, for example, solubility prediction, nucleation rates, crystal population balance modelling, etc) approaches. With recent progress in our understanding of crystal formation rates we can now realistically integrate both approaches to create small flow reactors integrating synthesis and crystallisation.
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Primary Supervisor: Katharina Edkins (Strathclyde)
Secondary Supervisor: Alison Nordon (Strathclyde)
Research challenges addressed in this project:
Crystallisation outcome, kinetics and particle size can all be influences by impurity in solution, so we need to quantify their influence to bring the next generation APIs faster to the market and the patient. In this project, we will systematically quantify interactions between the API and the impurity in solution to connect this with the crystallisation kinetics and outcome. We will use high-throughput spectroscopy and chemometrics (statistics) to derive overarching dependencies and formulate these into rules that are more generally applicable.
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Primary Supervisor: Alison Nordon (Strathclyde)
Secondary Supervisor: Jan Sefcik (Strathclyde)
Research challenges addressed in this project:
Multicomponent crystalline materials are of growing interest in the pharmaceutical industry, offering greater stability, enhanced bioavailability and simpler downstream processing compared to single component systems. However, their crystallisation is often poorly understood owing to the complexity of the crystallisation process, which typically involves introduction of the individual components into the crystalliser in different solvents.
This project will develop a small-scale screening and scale-up platform to advance fundamental understanding of multicomponent crystallisation processes. This will enable rapid process design, efficient scale-up and reduced materials usage, supporting sustainable manufacturing of next-generation medicines.
Working at the interface of chemistry and chemical engineering, you’ll gain expertise in crystallisation science, analytical chemistry, process monitoring and data modelling. You’ll also have opportunities to collaborate with industry and apply your research to industrial case studies.
Research Challenge Team 3: Drug product to patient
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Primary Supervisor: Iain Oswald (Strathclyde)
Secondary Supervisors: John Robertson
Research challenges addressed in this project:
This project will investigate the use of pressure on amorphous materials as a method to enhance the stability. This project will investigate the use of automation to sequentially probe the high-pressure behaviour of the amorphous materials using THz-Raman and correlate these observations with the known behaviour of model systems before investigating new systems. Advances in the integration of feedback loops as part of wider CMAC activities demonstrate that this type of iterative approach can yield products that have had optimal pressure treatment to stabilise the amorphous materials.
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Primary Supervisor: Alastair Florence (Strathclyde)
Secondary Supervisors: Michael Devlin (Strathclyde)
Research challenges addressed in this project:
This PhD will develop a next-generation Amorphous Screening DataFactory for co-amorphous materials, delivering new insights into structure–property relationships and enabling faster, data-driven component selection for downstream processes. The project will generate novel datasets and predictive models that enhance CMAC’s Knowledge Graph and expand CMAC's suite of drug substance predictive tools.
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Primary Supervisor: Nausheen Basha (Imperial College London)
Secondary Supervisor: Omar Matar (Imperial College London)
Research challenges addressed in this project:
This project bridges AI, computational fluid dynamics, and pharmaceutical science to revolutionise how we predict oral drug performance. You will develop geometry-conditioned deep learning models that learn how drug dissolution and precipitation behave inside patient-specific gastrointestinal geometries derived from MRI or CT data. By combining machine learning with physics-based simulations, you’ll create fast models on patient-centric drug dissolution dynamics. This project offers the opportunity to work at the cutting edge of pharma innovation, geometry-aware AI, and personalised medicine.
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Primary Supervisor: Hannah Batchelor (Strathclyde)
Secondary Supervisor: Alison Nordon (Strathclyde)
Research challenges addressed in this project:
Poorly soluble drugs remain one of the biggest challenges in oral medicine design, often requiring advanced formulations such as Amorphous Solid Dispersions (ASDs). Understanding how these drugs dissolve and precipitate under biorelevant conditions is critical—but current models like TIM-1 are large, expensive, and unsuitable for rapid early-stage screening. This PhD will develop a miniaturised, automated platform that combines process analytical technologies (PAT) such as Raman spectroscopy and UV-Vis with a digital twin, enabling real-time monitoring and predictive modelling of drug behaviour in simulated gastrointestinal environments. By creating a scalable, cost-effective system that uses small volumes of complex biorelevant media, this research will accelerate formulation design, support data-driven decision-making, and deliver the next generation of medicines for patients. Comparative studies against full-scale systems will validate the platform’s predictive power, ensuring impact across academia and industry.
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Primary Supervisor: Daniel Markl (Strathclyde)
Secondary Supervisor: Mohammad Salehian (Strathclyde)
Research challenges addressed in this project:
This project will develop a Data-Driven Amorphous and Co-Amorphous API–Excipient Compatibility DataFactory, combining automated experimentation with machine learning to predict stability and performance from molecular structures. You’ll build a closed-loop cyber-physical platform that accelerates formulation screening and powers a large-scale compatibility knowledge base for next-generation pharmaceutical development.
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Primary Supervisor: Mo Zandi (Sheffield)
Secondary Supervisor: Rachel Smith (Sheffield)
Research challenges addressed in this project:
Join a cutting-edge PhD project that will revolutionise how co-amorphous medicines are developed, using small-scale experimentation, CFD and AI-driven models to design robust hot melt extrusion processes in days instead of months. Working closely with leading industrial partners, you’ll create fast, sustainable, data-efficient workflows that directly accelerate real medicines to patients - while gaining rare, highly marketable expertise at the interface of pharmaceutical science, process engineering, and digital manufacturing.